1. Field of the Invention
The invention generally relates to detection of abnormalities in systems, and, more particularly, to an autonomic system and method that detects and diagnoses system abnormalities by comparing current performance/workload measurements and a dynamically compiled history of performance/workload measurements.
2. Description of the Related Art
Abnormality detection is a core functionality required by many systems such as automated management frameworks. Often abnormality detection is based on violations of quality of service (QoS) goals that are defined by an administrator or service level agreement (SLA). However, these violations of QoS goals are generally not very useful for invoking corrective actions. For example, if a storage system is overloaded and in violation of its QoS goals, the storage system will not automatically move data from the overloaded storage device to a faster storage device. Additionally, while there are a number of systems that monitor system performance, these monitoring systems are rarely used for abnormality detection. For example, a number of management tools monitor run-time information but generally delete it after 4-7 days without analyzing or post-processing it for abnormality detection. Therefore, it would be advantageous to provide an autonomic abnormality detection device for a system that has a plurality of components. Specifically, it would be advantageous to provide an autonomic abnormality detection device that periodically determines current workload to performance characteristics for the different components of a system, detects abnormalities by comparing a current workload to performance characteristic to a dynamically compiled history of workload to performance characteristics, determines the possible causes of a detected abnormality and determines and implements corrective actions, as necessary.